Phantom AI provides automakers with affordable advanced driver-assistance systems that reduce accidents and enable future self-driving features. The company delivers a modular, software-based autonomous driving platform that combines computer vision, sensor fusion, and vehicle control to meet OEM requirements. Its stack is designed for integration by automotive OEMs and Tier 1 suppliers, allowing customers to select and customize components. Phantom AI positions its AI models and system architecture to lower cost compared with legacy solutions while targeting production-scale automotive deployment and regulatory standards.
Phantom AI provides automakers with affordable advanced driver-assistance systems that reduce accidents and enable future self-driving features. The company delivers a modular, software-based autonomous driving platform that combines computer vision, sensor fusion, and vehicle control to meet OEM requirements. Its stack is designed for integration by automotive OEMs and Tier 1 suppliers, allowing customers to select and customize components. Phantom AI positions its AI models and system architecture to lower cost compared with legacy solutions while targeting production-scale automotive deployment and regulatory standards.
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This position requires a balance of deep technical expertise, strong analytical thinking, and cross-functional collaboration
3–5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications
In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design
Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience
Strong proficiency in Python and a deep understanding of software design principles and development best practices
Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation
Practical experience with data pipelines, distributed training, and machine learning experiment management tools
Proven ability to work effectively in a collaborative, cross-functional team environment
Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods
Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon)
Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation
Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus
Strong programming experience in Python and/or C++ within Linux development environments
Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP)
What the job involves
We are seeking a highly skilled Senior Deep Learning Engineer to drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS)
The successful candidate will play a key role in designing cutting-edge neural network architectures, optimizing model performance, and ensuring reliable deployment on embedded platforms
Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems
Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms
Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions
Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment
Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs
Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration
Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management
